针对胰腺MRI分割，提出自适应加权联邦学习方法。
Adaptive Aggregation Weights for Federated Segmentation of Pancreas MRI
- 动态调整各机构贡献权重，缓解数据异构问题
- 跨多家医院测试，分割精度显著提升
- 兼顾隐私保护，适合医疗多中心协作
联邦学习（FL）可在不共享敏感数据的前提下实现跨机构协同建模，是医学影像任务的有力解决方案。然而，传统方法如联邦平均（FedAvg）因机构间成像协议与患者群体差异，难以在不同域间泛化。这一问题在胰腺MRI分割中尤为突出，解剖变异与成像伪影严重影响性能。本文对胰腺MRI分割的联邦学习算法进行了全面评估，并提出一种引入自适应聚合权重的新方法。通过动态调节各客户端在模型聚合中的贡献，该方法有效应对领域差异，提升跨异构数据集的泛化能力。实验表明，相比传统方法，本方法在多个医院（中心）的数据上均提升了分割精度，并显著降低了域偏移影响，同时保持了隐私保护特性。
原文摘要 · Abstract (English)
Federated learning (FL) enables collaborative model training across institutions without sharing sensitive data, making it an attractive solution for medical imaging tasks. However, traditional FL methods, such as Federated Averaging (FedAvg), face difficulties in generalizing across domains due to variations in imaging protocols and patient demographics across institutions. This challenge is particularly evident in pancreas MRI segmentation, where anatomical variability and imaging artifacts significantly impact performance. In this paper, we conduct a comprehensive evaluation of FL algorithms for pancreas MRI segmentation and introduce a novel approach that incorporates adaptive aggregation weights. By dynamically adjusting the contribution of each client during model aggregation, our method accounts for domain-specific differences and improves generalization across heterogeneous datasets. Experimental results demonstrate that our approach enhances segmentation accuracy and reduces the impact of domain shift compared to conventional FL methods while maintaining privacy-preserving capabilities. Significant performance improvements are observed across multiple hospitals (centers).
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